Are you too deciding on investing in Artificial Intelligence for your organization? And you have already identified a use case as well as a proven ROI? That’s great. However, you do not have a dataset ready, do you? Most brands are struggling to build an excellent AI-ready dataset, but it is not rocket science. With the best-proven ideas, you could easily begin building your data set.

Let’s discuss what is a Dataset:

A Dataset is a collection of data that corresponds to the contents of a single database table or a single statistical data matrix. In a dataset, every column of the table represents a specific variable where each row corresponds to the respective member of the data set.

Therefore, in Machine Learning projects, we need training data set. And it is crucial to train your data sets for utilizing the model for performing several actions.

Why you need data set?

Machine Learning depends on data massively, without data Artificial Intelligence cannot learn. It is the significant aspect that makes algorithm training possible. No matter how great your AI team is your project might fail.

Here are three simple ways to get started with training data for your AI or Machine Learning Models:

1. Free Sources 

Free sources provide datasets for free. And there are multiple directories, portals, search engines, forums, and websites to source your datasets. These sources might be archives, public, or data that have been made public after several years with explicit permissions. Have a look at these sources for your quick reference

• Kaggle: It is an important resource for data scientists and Machine Learning experts. These data sets are of good quality available in multiple formats and also easily downloadable

• Public and Government Datasets: These are some of the prominent resources offering datasets from the industrial sector like agricultural agencies, complex networks, and more. This data are highly recommendable as these are relevant and available through government websites.  

2. Internal Sources

Yet another significant data source is the internal databases. You might not find what you are searching for in a free source. And in this situation, you should look into your organizational data. Precisely recent data might be relevant to your projects.

Hence, you should customize your data for various use cases. And internal sources could be the data that are produced from your social media handles, CRM, and web analytics.

3. Paid Sources

Unique datasets are not available for free or in internal sources. Hence you have to obtain it through paid sources. Paid sources are built by brands that work closely on generating datasets that you require for your projects according to your specific requirements and needs.

Hence, you need data annotation. Data annotation is a process of adding additional data like description and metadata to your datasets for making them easily recognizable by the machine. No matter where your data comes from, it will be in raw format. To begin with, it has to be cleaned and then annotated using precision techniques for ensuring AI training data for your Machine Learning models.

Data annotation is the best strategy for training your datasets. So, if you are looking for an ideal data annotation service provider, Data Labeler is your go-to guy.

Data Labeler- The Best Human-Powered Data Labeling and Annotation Services

Data Labeler empowers you with accurate, personalized, and quality-labeled datasets for your AI and Machine Learning initiatives.

We at Data Labeler provide options for real-time labeling and guidance on labeling with our robust workforce management.

Contact Us now – sales@datalabeler.com